Carga y limpieza preliminar de los datos

Los datos que se van a analizar en este documento proceden de la compilación hecha por usuarios de Kaggle. La fecha del análisis empieza el 22 de agosto de 2022, utilizando la versión 166 recopilada en la web anterior.

Cargar el dataset correctamente

Carga del dataset desde Python

import pandas as pd
datos = pd.read_csv("data/covid_19_clean_complete.csv")
datos.head(10)

##                  Province/State  ...             WHO Region
## 0                           NaN  ...  Eastern Mediterranean
## 1                           NaN  ...                 Europe
## 2                           NaN  ...                 Africa
## 3                           NaN  ...                 Europe
## 4                           NaN  ...                 Africa
## 5                           NaN  ...               Americas
## 6                           NaN  ...               Americas
## 7                           NaN  ...                 Europe
## 8  Australian Capital Territory  ...        Western Pacific
## 9               New South Wales  ...        Western Pacific
## 
## [10 rows x 10 columns]

Carga del dataset con la librería reticulate

pd <- import("pandas")
datos <- pd$read_csv("data/covid_19_clean_complete.csv")
kable(head(datos, 10))

Province/State	Country/Region	Lat	Long	Date	WHO Region
NaN	Afghanistan	33.93911	67.70995	2020-01-22	Eastern Mediterranean
NaN	Albania	41.15330	20.16830	2020-01-22	Europe
NaN	Algeria	28.03390	1.65960	2020-01-22	Africa
NaN	Andorra	42.50630	1.52180	2020-01-22	Europe
NaN	Angola	-11.20270	17.87390	2020-01-22	Africa
NaN	Antigua and Barbuda	17.06080	-61.79640	2020-01-22	Americas
NaN	Argentina	-38.41610	-63.61670	2020-01-22	Americas
NaN	Armenia	40.06910	45.03820	2020-01-22	Europe
Australian Capital Territory	Australia	-35.47350	149.01240	2020-01-22	Western Pacific
New South Wales	Australia	-33.86880	151.20930	2020-01-22	Western Pacific

Carga del dataset desde R

datos <- read.csv("data/covid_19_clean_complete.csv", stringsAsFactors = T)
datos %>% head(10) %>% kable()

Province.State	Country.Region	Lat	Long	Date	WHO.Region
	Afghanistan	33.93911	67.70995	2020-01-22	Eastern Mediterranean
	Albania	41.15330	20.16830	2020-01-22	Europe
	Algeria	28.03390	1.65960	2020-01-22	Africa
	Andorra	42.50630	1.52180	2020-01-22	Europe
	Angola	-11.20270	17.87390	2020-01-22	Africa
	Antigua and Barbuda	17.06080	-61.79640	2020-01-22	Americas
	Argentina	-38.41610	-63.61670	2020-01-22	Americas
	Armenia	40.06910	45.03820	2020-01-22	Europe
Australian Capital Territory	Australia	-35.47350	149.01240	2020-01-22	Western Pacific
New South Wales	Australia	-33.86880	151.20930	2020-01-22	Western Pacific

Estructura de los datos y cambio nombre de las columnas

str(datos)

## 'data.frame':    49068 obs. of  10 variables:
##  $ Province.State: Factor w/ 79 levels "","Alberta","Anguilla",..: 1 1 1 1 1 1 1 1 6 47 ...
##  $ Country.Region: Factor w/ 187 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 9 ...
##  $ Lat           : num  33.9 41.2 28 42.5 -11.2 ...
##  $ Long          : num  67.71 20.17 1.66 1.52 17.87 ...
##  $ Date          : Factor w/ 188 levels "2020-01-22","2020-01-23",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Confirmed     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Deaths        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Recovered     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Active        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ WHO.Region    : Factor w/ 6 levels "Africa","Americas",..: 3 4 1 4 1 2 2 4 6 6 ...

colnames(datos) = c("Provincia_Estado",
                    "Pais_Region",
                    "Latitud", # N+ o S-
                    "Longitud", # E+ o W-
                    "Fecha",
                    "Casos_Confirmados",
                    "Casos_Muertos",
                    "Casos_Recuperados",
                    "Casos_Activos",
                    "WHO_Region"
                    )
datos %>% head() %>% kable() # %>% kable_styling()

Pais_Region	Latitud	Longitud	Fecha	WHO_Region
Afghanistan	33.93911	67.70995	2020-01-22	Eastern Mediterranean
Albania	41.15330	20.16830	2020-01-22	Europe
Algeria	28.03390	1.65960	2020-01-22	Africa
Andorra	42.50630	1.52180	2020-01-22	Europe
Angola	-11.20270	17.87390	2020-01-22	Africa
Antigua and Barbuda	17.06080	-61.79640	2020-01-22	Americas

Tipo de datos de cada columna

Cualitativas se convierten factor o bien as.factor.
Ordinales se convierten con ordered.
Cuantitativas se convierten con as.numeric.

El tipo de dato fecha y su manipulación

Cambiar la columna fecha a tipo Date:

#datos$Fecha %<>% as.Date(format="%Y-%m-%d")
datos$Fecha %<>% ymd() # Con librería lubridate
str(datos)

## 'data.frame':    49068 obs. of  10 variables:
##  $ Provincia_Estado : Factor w/ 79 levels "","Alberta","Anguilla",..: 1 1 1 1 1 1 1 1 6 47 ...
##  $ Pais_Region      : Factor w/ 187 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 9 ...
##  $ Latitud          : num  33.9 41.2 28 42.5 -11.2 ...
##  $ Longitud         : num  67.71 20.17 1.66 1.52 17.87 ...
##  $ Fecha            : Date, format: "2020-01-22" "2020-01-22" ...
##  $ Casos_Confirmados: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Casos_Muertos    : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Casos_Recuperados: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Casos_Activos    : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ WHO_Region       : Factor w/ 6 levels "Africa","Americas",..: 3 4 1 4 1 2 2 4 6 6 ...

\[Casos\ Confirmados = Muertos + Recuperados + Enfermos\]

# Lo siguiente da lo mismo que la columna Casos_Activos, pero en el dataset que se
# utilizó en el curso no aparecía
datos %<>% # Ventaja que nos ofrece la librería magrittr
  mutate(Casos_Enfermos = Casos_Confirmados - Casos_Muertos - Casos_Recuperados)

datos %>%
  filter(Casos_Confirmados > 10000) %>%
  head() %>%
  kable()

Provincia_Estado	Pais_Region	Latitud	Longitud	Fecha	Casos_Confirmados	Casos_Muertos	Casos_Recuperados	Casos_Activos	WHO_Region	Casos_Enfermos
Hubei	China	30.9756	112.2707	2020-02-02	11177	350	295	10532	Western Pacific	10532
Hubei	China	30.9756	112.2707	2020-02-03	13522	414	386	12722	Western Pacific	12722
Hubei	China	30.9756	112.2707	2020-02-04	16678	479	522	15677	Western Pacific	15677
Hubei	China	30.9756	112.2707	2020-02-05	19665	549	633	18483	Western Pacific	18483
Hubei	China	30.9756	112.2707	2020-02-06	22112	618	817	20677	Western Pacific	20677
Hubei	China	30.9756	112.2707	2020-02-07	24953	699	1115	23139	Western Pacific	23139

datos %>%
  filter(Casos_Enfermos < 0) %>%
  arrange(Provincia_Estado, Fecha) %>%
  kable()

Provincia_Estado	Pais_Region	Latitud	Longitud	Fecha	Casos_Confirmados	Casos_Muertos	Casos_Recuperados	Casos_Activos	WHO_Region	Casos_Enfermos
	Liechtenstein	47.140000	9.55000	2020-06-23	82	2	81	-1	Europe	-1
	Uganda	1.373333	32.29028	2020-07-20	1069	0	1071	-2	Africa	-2
Channel Islands	United Kingdom	49.372300	-2.36440	2020-05-23	558	45	515	-2	Europe	-2
Channel Islands	United Kingdom	49.372300	-2.36440	2020-05-24	558	45	517	-4	Europe	-4
Channel Islands	United Kingdom	49.372300	-2.36440	2020-05-25	559	45	517	-3	Europe	-3
Channel Islands	United Kingdom	49.372300	-2.36440	2020-05-30	560	45	525	-10	Europe	-10
Channel Islands	United Kingdom	49.372300	-2.36440	2020-05-31	560	45	528	-13	Europe	-13
Channel Islands	United Kingdom	49.372300	-2.36440	2020-06-01	560	45	528	-13	Europe	-13
Channel Islands	United Kingdom	49.372300	-2.36440	2020-06-02	560	46	528	-14	Europe	-14
Hainan	China	19.195900	109.74530	2020-03-24	168	6	168	-6	Western Pacific	-6
Hainan	China	19.195900	109.74530	2020-03-25	168	6	168	-6	Western Pacific	-6
Hainan	China	19.195900	109.74530	2020-03-26	168	6	168	-6	Western Pacific	-6
Hainan	China	19.195900	109.74530	2020-03-27	168	6	168	-6	Western Pacific	-6
Hainan	China	19.195900	109.74530	2020-03-28	168	6	168	-6	Western Pacific	-6
Hainan	China	19.195900	109.74530	2020-03-29	168	6	168	-6	Western Pacific	-6
Hainan	China	19.195900	109.74530	2020-03-30	168	6	168	-6	Western Pacific	-6
Hainan	China	19.195900	109.74530	2020-03-31	168	6	168	-6	Western Pacific	-6
Hainan	China	19.195900	109.74530	2020-04-01	168	6	168	-6	Western Pacific	-6

datos %>%
  filter(Provincia_Estado == "Hainan") %>%
  kable()

Provincia_Estado	Pais_Region	Latitud	Longitud	Fecha	Casos_Confirmados	Casos_Muertos	Casos_Recuperados	Casos_Activos	WHO_Region	Casos_Enfermos
Hainan	China	19.1959	109.7453	2020-01-22	4	0	0	4	Western Pacific	4
Hainan	China	19.1959	109.7453	2020-01-23	5	0	0	5	Western Pacific	5
Hainan	China	19.1959	109.7453	2020-01-24	8	0	0	8	Western Pacific	8
Hainan	China	19.1959	109.7453	2020-01-25	19	0	0	19	Western Pacific	19
Hainan	China	19.1959	109.7453	2020-01-26	22	0	0	22	Western Pacific	22
Hainan	China	19.1959	109.7453	2020-01-27	33	1	0	32	Western Pacific	32
Hainan	China	19.1959	109.7453	2020-01-28	40	1	0	39	Western Pacific	39
Hainan	China	19.1959	109.7453	2020-01-29	43	1	0	42	Western Pacific	42
Hainan	China	19.1959	109.7453	2020-01-30	46	1	1	44	Western Pacific	44
Hainan	China	19.1959	109.7453	2020-01-31	52	1	1	50	Western Pacific	50
Hainan	China	19.1959	109.7453	2020-02-01	62	1	1	60	Western Pacific	60
Hainan	China	19.1959	109.7453	2020-02-02	64	1	4	59	Western Pacific	59
Hainan	China	19.1959	109.7453	2020-02-03	72	1	4	67	Western Pacific	67
Hainan	China	19.1959	109.7453	2020-02-04	80	1	5	74	Western Pacific	74
Hainan	China	19.1959	109.7453	2020-02-05	99	1	5	93	Western Pacific	93
Hainan	China	19.1959	109.7453	2020-02-06	106	1	8	97	Western Pacific	97
Hainan	China	19.1959	109.7453	2020-02-07	117	2	10	105	Western Pacific	105
Hainan	China	19.1959	109.7453	2020-02-08	124	2	14	108	Western Pacific	108
Hainan	China	19.1959	109.7453	2020-02-09	131	3	19	109	Western Pacific	109
Hainan	China	19.1959	109.7453	2020-02-10	138	3	19	116	Western Pacific	116
Hainan	China	19.1959	109.7453	2020-02-11	144	3	20	121	Western Pacific	121
Hainan	China	19.1959	109.7453	2020-02-12	157	4	27	126	Western Pacific	126
Hainan	China	19.1959	109.7453	2020-02-13	157	4	30	123	Western Pacific	123
Hainan	China	19.1959	109.7453	2020-02-14	159	4	43	112	Western Pacific	112
Hainan	China	19.1959	109.7453	2020-02-15	162	4	39	119	Western Pacific	119
Hainan	China	19.1959	109.7453	2020-02-16	162	4	52	106	Western Pacific	106
Hainan	China	19.1959	109.7453	2020-02-17	163	4	59	100	Western Pacific	100
Hainan	China	19.1959	109.7453	2020-02-18	163	4	79	80	Western Pacific	80
Hainan	China	19.1959	109.7453	2020-02-19	168	4	84	80	Western Pacific	80
Hainan	China	19.1959	109.7453	2020-02-20	168	4	86	78	Western Pacific	78
Hainan	China	19.1959	109.7453	2020-02-21	168	4	95	69	Western Pacific	69
Hainan	China	19.1959	109.7453	2020-02-22	168	4	104	60	Western Pacific	60
Hainan	China	19.1959	109.7453	2020-02-23	168	5	106	57	Western Pacific	57
Hainan	China	19.1959	109.7453	2020-02-24	168	5	116	47	Western Pacific	47
Hainan	China	19.1959	109.7453	2020-02-25	168	5	124	39	Western Pacific	39
Hainan	China	19.1959	109.7453	2020-02-26	168	5	129	34	Western Pacific	34
Hainan	China	19.1959	109.7453	2020-02-27	168	5	131	32	Western Pacific	32
Hainan	China	19.1959	109.7453	2020-02-28	168	5	133	30	Western Pacific	30
Hainan	China	19.1959	109.7453	2020-02-29	168	5	148	15	Western Pacific	15
Hainan	China	19.1959	109.7453	2020-03-01	168	5	149	14	Western Pacific	14
Hainan	China	19.1959	109.7453	2020-03-02	168	5	151	12	Western Pacific	12
Hainan	China	19.1959	109.7453	2020-03-03	168	5	155	8	Western Pacific	8
Hainan	China	19.1959	109.7453	2020-03-04	168	5	158	5	Western Pacific	5
Hainan	China	19.1959	109.7453	2020-03-05	168	6	158	4	Western Pacific	4
Hainan	China	19.1959	109.7453	2020-03-06	168	6	158	4	Western Pacific	4
Hainan	China	19.1959	109.7453	2020-03-07	168	6	158	4	Western Pacific	4
Hainan	China	19.1959	109.7453	2020-03-08	168	6	159	3	Western Pacific	3
Hainan	China	19.1959	109.7453	2020-03-09	168	6	159	3	Western Pacific	3
Hainan	China	19.1959	109.7453	2020-03-10	168	6	159	3	Western Pacific	3
Hainan	China	19.1959	109.7453	2020-03-11	168	6	159	3	Western Pacific	3
Hainan	China	19.1959	109.7453	2020-03-12	168	6	160	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-03-13	168	6	160	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-03-14	168	6	160	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-03-15	168	6	160	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-03-16	168	6	161	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-03-17	168	6	161	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-03-18	168	6	161	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-03-19	168	6	161	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-03-20	168	6	161	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-03-21	168	6	161	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-03-22	168	6	161	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-03-23	168	6	161	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-03-24	168	6	168	-6	Western Pacific	-6
Hainan	China	19.1959	109.7453	2020-03-25	168	6	168	-6	Western Pacific	-6
Hainan	China	19.1959	109.7453	2020-03-26	168	6	168	-6	Western Pacific	-6
Hainan	China	19.1959	109.7453	2020-03-27	168	6	168	-6	Western Pacific	-6
Hainan	China	19.1959	109.7453	2020-03-28	168	6	168	-6	Western Pacific	-6
Hainan	China	19.1959	109.7453	2020-03-29	168	6	168	-6	Western Pacific	-6
Hainan	China	19.1959	109.7453	2020-03-30	168	6	168	-6	Western Pacific	-6
Hainan	China	19.1959	109.7453	2020-03-31	168	6	168	-6	Western Pacific	-6
Hainan	China	19.1959	109.7453	2020-04-01	168	6	168	-6	Western Pacific	-6
Hainan	China	19.1959	109.7453	2020-04-02	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-03	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-04	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-05	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-06	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-07	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-08	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-09	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-10	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-11	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-12	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-13	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-14	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-15	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-16	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-17	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-18	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-19	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-20	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-21	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-22	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-23	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-24	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-25	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-26	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-27	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-28	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-29	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-04-30	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-01	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-02	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-03	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-04	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-05	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-06	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-07	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-08	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-09	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-10	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-11	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-12	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-13	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-14	168	6	162	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-15	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-16	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-17	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-18	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-19	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-20	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-21	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-22	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-23	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-24	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-25	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-26	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-27	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-28	169	6	162	1	Western Pacific	1
Hainan	China	19.1959	109.7453	2020-05-29	169	6	163	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-30	169	6	163	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-05-31	169	6	163	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-01	169	6	163	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-02	169	6	163	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-03	169	6	163	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-04	169	6	163	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-05	169	6	163	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-06	170	6	162	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-07	170	6	162	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-08	170	6	162	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-09	170	6	162	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-10	170	6	162	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-11	170	6	162	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-12	171	6	162	3	Western Pacific	3
Hainan	China	19.1959	109.7453	2020-06-13	171	6	162	3	Western Pacific	3
Hainan	China	19.1959	109.7453	2020-06-14	171	6	162	3	Western Pacific	3
Hainan	China	19.1959	109.7453	2020-06-15	171	6	163	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-16	171	6	163	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-17	171	6	163	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-18	171	6	163	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-19	171	6	163	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-20	171	6	163	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-21	171	6	163	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-22	171	6	163	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-23	171	6	163	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-24	171	6	163	2	Western Pacific	2
Hainan	China	19.1959	109.7453	2020-06-25	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-26	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-27	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-28	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-29	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-06-30	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-01	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-02	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-03	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-04	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-05	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-06	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-07	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-08	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-09	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-10	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-11	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-12	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-13	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-14	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-15	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-16	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-17	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-18	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-19	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-20	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-21	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-22	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-23	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-24	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-25	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-26	171	6	165	0	Western Pacific	0
Hainan	China	19.1959	109.7453	2020-07-27	171	6	165	0	Western Pacific	0

Datos anómalos y sin sentido

# Corregir datos anómalos y sin sentido
datos %>%
  filter(Provincia_Estado == "Hainan", Casos_Enfermos < 0) %>%
  mutate(Casos_Recuperados = Casos_Recuperados + Casos_Enfermos,
         Casos_Enfermos = 0) %>%
  kable()

Provincia_Estado	Pais_Region	Latitud	Longitud	Fecha	Casos_Confirmados	Casos_Muertos	Casos_Recuperados	Casos_Activos	WHO_Region
Hainan	China	19.1959	109.7453	2020-03-24	168	6	162	-6	Western Pacific
Hainan	China	19.1959	109.7453	2020-03-25	168	6	162	-6	Western Pacific
Hainan	China	19.1959	109.7453	2020-03-26	168	6	162	-6	Western Pacific
Hainan	China	19.1959	109.7453	2020-03-27	168	6	162	-6	Western Pacific
Hainan	China	19.1959	109.7453	2020-03-28	168	6	162	-6	Western Pacific
Hainan	China	19.1959	109.7453	2020-03-29	168	6	162	-6	Western Pacific
Hainan	China	19.1959	109.7453	2020-03-30	168	6	162	-6	Western Pacific
Hainan	China	19.1959	109.7453	2020-03-31	168	6	162	-6	Western Pacific
Hainan	China	19.1959	109.7453	2020-04-01	168	6	162	-6	Western Pacific

Análisis geográfico de los datos

#datos_europa = datos[datos$Latitud > 38 & datos$Longitud > -25 & datos$Longitud < 30 , ]
datos_europa = datos %>%
  filter(Latitud > 38, between(Longitud, -25, 30))

nrow(datos_europa)

## [1] 8460

table(datos_europa$Pais_Region) %>%
  as.data.frame() %>%
  filter(Freq > 0) %>%
  kable()

Var1	Freq
Albania	188
Andorra	188
Austria	188
Belarus	188
Belgium	188
Bosnia and Herzegovina	188
Bulgaria	188
Croatia	188
Czechia	188
Denmark	376
Estonia	188
Finland	188
France	188
Germany	188
Greece	188
Holy See	188
Hungary	188
Iceland	188
Ireland	188
Italy	188
Kosovo	188
Latvia	188
Liechtenstein	188
Lithuania	188
Luxembourg	188
Moldova	188
Monaco	188
Montenegro	188
Netherlands	188
North Macedonia	188
Norway	188
Poland	188
Portugal	188
Romania	188
San Marino	188
Serbia	188
Slovakia	188
Slovenia	188
Spain	188
Sweden	188
Switzerland	188
United Kingdom	564

datos_europa %>%
  filter(Fecha == ymd("2020-03-15")) %>%
  kable()

Provincia_Estado	Pais_Region	Latitud	Longitud	Fecha	Casos_Confirmados	Casos_Muertos	Casos_Recuperados	Casos_Activos	WHO_Region	Casos_Enfermos
	Albania	41.15330	20.168300	2020-03-15	42	1	0	41	Europe	41
	Andorra	42.50630	1.521800	2020-03-15	1	0	1	0	Europe	0
	Austria	47.51620	14.550100	2020-03-15	860	1	6	853	Europe	853
	Belarus	53.70980	27.953400	2020-03-15	27	0	3	24	Europe	24
	Belgium	50.83330	4.469936	2020-03-15	886	4	1	881	Europe	881
	Bosnia and Herzegovina	43.91590	17.679100	2020-03-15	24	0	0	24	Europe	24
	Bulgaria	42.73390	25.485800	2020-03-15	51	2	0	49	Europe	49
	Croatia	45.10000	15.200000	2020-03-15	49	0	1	48	Europe	48
	Czechia	49.81750	15.473000	2020-03-15	253	0	0	253	Europe	253
Faroe Islands	Denmark	61.89260	-6.911800	2020-03-15	11	0	0	11	Europe	11
	Denmark	56.26390	9.501800	2020-03-15	864	2	1	861	Europe	861
	Estonia	58.59530	25.013600	2020-03-15	171	0	1	170	Europe	170
	Finland	61.92411	25.748151	2020-03-15	244	0	10	234	Europe	234
	France	46.22760	2.213700	2020-03-15	4499	91	12	4396	Europe	4396
	Germany	51.16569	10.451526	2020-03-15	5795	11	46	5738	Europe	5738
	Greece	39.07420	21.824300	2020-03-15	331	4	8	319	Europe	319
	Holy See	41.90290	12.453400	2020-03-15	1	0	0	1	Europe	1
	Hungary	47.16250	19.503300	2020-03-15	32	1	1	30	Europe	30
	Iceland	64.96310	-19.020800	2020-03-15	171	5	8	158	Europe	158
	Ireland	53.14240	-7.692100	2020-03-15	129	2	0	127	Europe	127
	Italy	41.87194	12.567380	2020-03-15	24747	1809	2335	20603	Europe	20603
	Latvia	56.87960	24.603200	2020-03-15	30	0	1	29	Europe	29
	Liechtenstein	47.14000	9.550000	2020-03-15	4	0	1	3	Europe	3
	Lithuania	55.16940	23.881300	2020-03-15	12	0	1	11	Europe	11
	Luxembourg	49.81530	6.129600	2020-03-15	59	1	0	58	Europe	58
	Moldova	47.41160	28.369900	2020-03-15	23	0	0	23	Europe	23
	Monaco	43.73330	7.416700	2020-03-15	2	0	0	2	Europe	2
	Montenegro	42.70868	19.374390	2020-03-15	0	0	0	0	Europe	0
	Netherlands	52.13260	5.291300	2020-03-15	1135	20	0	1115	Europe	1115
	North Macedonia	41.60860	21.745300	2020-03-15	14	0	1	13	Europe	13
	Norway	60.47200	8.468900	2020-03-15	1221	3	1	1217	Europe	1217
	Poland	51.91940	19.145100	2020-03-15	119	3	0	116	Europe	116
	Portugal	39.39990	-8.224500	2020-03-15	245	0	2	243	Europe	243
	Romania	45.94320	24.966800	2020-03-15	131	0	9	122	Europe	122
	San Marino	43.94240	12.457800	2020-03-15	101	5	4	92	Europe	92
	Serbia	44.01650	21.005900	2020-03-15	48	0	0	48	Europe	48
	Slovakia	48.66900	19.699000	2020-03-15	54	0	0	54	Europe	54
	Slovenia	46.15120	14.995500	2020-03-15	219	1	0	218	Europe	218
	Spain	40.46367	-3.749220	2020-03-15	7798	289	517	6992	Europe	6992
	Sweden	60.12816	18.643501	2020-03-15	1022	3	0	1019	Europe	1019
	Switzerland	46.81820	8.227500	2020-03-15	2200	14	4	2182	Europe	2182
Channel Islands	United Kingdom	49.37230	-2.364400	2020-03-15	3	0	0	3	Europe	3
Isle of Man	United Kingdom	54.23610	-4.548100	2020-03-15	0	0	0	0	Europe	0
	United Kingdom	55.37810	-3.436000	2020-03-15	3072	43	18	3011	Europe	3011
	Kosovo	42.60264	20.902977	2020-03-15	0	0	0	0	Europe	0

Ejercicio práctico: mi viaje a Potsman

Distancia euclídea:

\[d(x, y) = \sqrt{(x_{Lat} - y_{Lat})^2 + (x_{Long} - y_{Long})^2}\]

distancia_grados <- function(x, y){
  sqrt((x[1] - y[1])^2 + (x[2] - y[2])^2)
}

distancia_grados_potsdam <- function(x){
  potsdam = c(52.366956, 13.906734)
  distancia_grados(x, potsdam)
}

dist_potsdam <- apply(cbind(datos_europa$Latitud, datos_europa$Longitud),
                      MARGIN = 1,
                      FUN = distancia_grados_potsdam)

datos_europa %<>%
  mutate(dist_potsdam = dist_potsdam)

datos_europa %>%
  filter(between(Fecha, dmy("02-03-2020"), dmy("07-03-2020")),
         dist_potsdam < 4) %>% # Radio menor de 4 grados
  arrange(Pais_Region) %>% # Ordenar por país
  kable()

Pais_Region	Latitud	Longitud	Fecha	Casos_Confirmados	Casos_Recuperados	Casos_Activos	WHO_Region	Casos_Enfermos	dist_potsdam
Czechia	49.81750	15.47300	2020-03-02	3	0	3	Europe	3	2.992142
Czechia	49.81750	15.47300	2020-03-03	5	0	5	Europe	5	2.992142
Czechia	49.81750	15.47300	2020-03-04	8	0	8	Europe	8	2.992142
Czechia	49.81750	15.47300	2020-03-05	12	0	12	Europe	12	2.992142
Czechia	49.81750	15.47300	2020-03-06	18	0	18	Europe	18	2.992142
Czechia	49.81750	15.47300	2020-03-07	19	0	19	Europe	19	2.992142
Germany	51.16569	10.45153	2020-03-02	159	16	143	Europe	143	3.658073
Germany	51.16569	10.45153	2020-03-03	196	16	180	Europe	180	3.658073
Germany	51.16569	10.45153	2020-03-04	262	16	246	Europe	246	3.658073
Germany	51.16569	10.45153	2020-03-05	482	16	466	Europe	466	3.658073
Germany	51.16569	10.45153	2020-03-06	670	17	653	Europe	653	3.658073
Germany	51.16569	10.45153	2020-03-07	799	18	781	Europe	781	3.658073

Mapas del mundo con rnaturalearth

# Antes se necesita instalar rnaturalearthdata
#install.packages("rnaturalearthdata")
world <- ne_countries(scale = "medium", returnclass = "sf")

datos$Pais_Region = factor(datos$Pais_Region, levels = c(levels(datos$Pais_Region), "United States"))
datos[datos$Pais_Region == "US", ]$Pais_Region = "United States"

world %>% 
  inner_join(datos, by = c("name" = "Pais_Region")) %>%
  filter(Fecha == dmy("30-05-2020")) %>%
  ggplot() +
  geom_sf(color = "black", aes(fill = Casos_Confirmados)) +
  #coord_sf(crs = "+proj=laea +lat_0=50 +lon_0=10 +units=m +ellps=GRS80") +
  scale_fill_viridis_c(option = "plasma", trans = "sqrt") +
  xlab("Longitud") + ylab("Latitud") +
  ggtitle("Mapa del mundo",  subtitle = "COVID-19") -> g

ggplotly(g) # para hacer el mapa interativo

Faltan regiones por pintar porque no vienen estandarizados los países en los dos datasets. Se ha arreglado Estados Unidos, sería recomendable hacer lo mismo con los demás países faltantes.

Mapa minimalista con puntos

datos %>%
  filter(Fecha == dmy("30/03/2020")) %>%
  ggplot(aes(x = Longitud, y = Latitud)) +
  geom_point(aes(size = log(Casos_Confirmados + 1),
                 colour = log(Casos_Muertos + 1))) +
  coord_fixed() +
  theme(legend.position = "bottom") -> g

ggplotly(g) # para hacer el mapa interativo

Top de países infectados

thresh = 1000
datos %>%
  filter(Fecha == ymd("2020-04-05"),
         Casos_Confirmados > thresh) %>%
  mutate(Prop_Muertos = Casos_Muertos / Casos_Confirmados,
         Ranking = dense_rank(desc(Prop_Muertos))) %>%
  arrange(Ranking) %>%
  head(10) %>%
  kable()

Pais_Region	Latitud	Longitud	Fecha	Casos_Confirmados	Casos_Muertos	Casos_Recuperados	Casos_Activos	WHO_Region	Casos_Enfermos	Prop_Muertos	Ranking
Italy	41.87194	12.567380	2020-04-05	128948	15887	21815	91246	Europe	91246	0.1232047	1
Algeria	28.03390	1.659600	2020-04-05	1320	152	90	1078	Africa	1078	0.1151515	2
France	46.22760	2.213700	2020-04-05	70478	8078	16183	46217	Europe	46217	0.1146173	3
Netherlands	52.13260	5.291300	2020-04-05	17851	1766	0	16085	Europe	16085	0.0989300	4
United Kingdom	55.37810	-3.436000	2020-04-05	60792	5866	135	54791	Europe	54791	0.0964930	5
Spain	40.46367	-3.749220	2020-04-05	131646	12641	38080	80925	Europe	80925	0.0960227	6
Indonesia	-0.78930	113.921300	2020-04-05	2273	198	164	1911	South-East Asia	1911	0.0871095	7
Belgium	50.83330	4.469936	2020-04-05	19691	1447	3751	14493	Europe	14493	0.0734853	8
Morocco	31.79170	-7.092600	2020-04-05	1021	70	76	875	Eastern Mediterranean	875	0.0685602	9
Egypt	26.82055	30.802498	2020-04-05	1173	78	247	848	Eastern Mediterranean	848	0.0664962	10

Segmentación de los datos de Regiones por Categorías con mosaicplot

datos$lat_class = cut(datos$Latitud,
                      breaks = seq(from = -90, to = 90, by = 10))
datos$long_class = cut(datos$Longitud,
                       breaks = seq(from = -180, to = 180, by = 10))
tt = table(datos$lat_class, datos$long_class)
tt = tt[nrow(tt):1, ]
mosaicplot(t(tt), shade = T)

Análisis Temporal de los Datos

Evolución de Infectados vs Recuperados vs Muertos

datos_por_fecha = aggregate(
  cbind(Casos_Confirmados, Casos_Muertos, Casos_Recuperados) ~ Fecha,
                            data = datos,
                            FUN = sum)

datos_por_fecha$Casos_Enfermos = datos_por_fecha$Casos_Confirmados - datos_por_fecha$Casos_Muertos - datos_por_fecha$Casos_Recuperados
head(datos_por_fecha)

##        Fecha Casos_Confirmados Casos_Muertos Casos_Recuperados Casos_Enfermos
## 1 2020-01-22               555            17                28            510
## 2 2020-01-23               654            18                30            606
## 3 2020-01-24               941            26                36            879
## 4 2020-01-25              1434            42                39           1353
## 5 2020-01-26              2118            56                52           2010
## 6 2020-01-27              2927            82                61           2784

tail(datos_por_fecha)

##          Fecha Casos_Confirmados Casos_Muertos Casos_Recuperados Casos_Enfermos
## 183 2020-07-22          15227725        623540           8541255        6062930
## 184 2020-07-23          15510481        633506           8710969        6166006
## 185 2020-07-24          15791645        639650           8939705        6212290
## 186 2020-07-25          16047190        644517           9158743        6243930
## 187 2020-07-26          16251796        648621           9293464        6309711
## 188 2020-07-27          16480485        654036           9468087        6358362

barplot(Casos_Confirmados ~ Fecha, data = datos_por_fecha)

plot(Casos_Confirmados ~ Fecha, data = datos_por_fecha,
     col = "blue", type = "l", main = "Casos documentados por día en todo el mundo",
     xlab = "Fecha", ylab = "Número de personas", log = "y")
lines(Casos_Muertos ~ Fecha, data = datos_por_fecha, col = "red")
lines(Casos_Recuperados ~ Fecha, data = datos_por_fecha, col = "green")
legend("topleft", c("Confirmados", "Muertos", "Recuperados"),
       col = c("blue", "red", "green"), pch = 1, lwd = 2)

Análisis de los datos de España

datos_spain = datos %>%
  filter(Pais_Region == "Spain") %>%
  select(Fecha, starts_with("Casos_"))

plot(x = datos_spain$Fecha, y = datos_spain$Casos_Confirmados,
     main = "Datos confirmados en España", type = "s",
     col = "blue", lwd = 2)

barplot(as.matrix(t(datos_spain[, 3:5])),
        names = datos_spain$Fecha,
        col = c("red", "green", "yellow"),
        main = "Estudio de casos por tipo en España",
        xlab = "Fecha",
        ylab = "Número de personas")
legend("topleft", c("Muertos", "Recuperados", "Enfermos"),
       col = c("red", "green", "yellow"), lwd = 2, pch = 1)

Librería xts en el Análisis Temporal

datos_por_fecha_ts <- xts(x = datos_por_fecha[, 2:5],
                          order.by = datos_por_fecha$Fecha)
dygraph(datos_por_fecha_ts) %>%
  dyOptions(labelsUTC = T, labelsKMB = T,
            fillGraph = T, fillAlpha = 0.05,
            drawGrid = F, colors = "#FF2D55") %>%
  dyRangeSelector() %>%
  dyCrosshair(direction = "vertical") %>%
  dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2,
              hideOnMouseOut = F) %>%
  dyRoller(rollPeriod = 2)

datos_spain = datos %>%
  filter(Pais_Region == "Spain") %>%
  select(Fecha, starts_with("Casos_"))

plot(x = datos_spain$Fecha, y = datos_spain$Casos_Confirmados,
     main = "Datos confirmados en España", type = "s",
     col = "blue", lwd = 2)

datos_por_fecha_ts <- xts(x = datos_spain[, 2:5],
                          order.by = datos_spain$Fecha)
dygraph(datos_por_fecha_ts) %>%
  dyOptions(labelsUTC = T, labelsKMB = T,
            fillGraph = T, drawGrid = F, colors = "#FF2D55") %>%
  dyRangeSelector() %>%
  dyCrosshair(direction = "vertical") %>%
  dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2,
              hideOnMouseOut = F) %>%
  dyRoller(rollPeriod = 2)

Lead, lag y estudio de nuevos casos

datos_spain %<>%
  mutate(Nuevos_Casos_Confirmados = Casos_Confirmados - lag(Casos_Confirmados, n = 1),
         Nuevos_Casos_Muertos = Casos_Muertos - lag(Casos_Muertos, n = 1),
         Nuevos_Casos_Recuperados = Casos_Recuperados - lag(Casos_Recuperados, n = 1)
         )

plot(Nuevos_Casos_Confirmados ~ Fecha, data = datos_spain[datos_spain$Fecha <= "2020-04-12", ],
     type = "l", col ="blue", 
     xlab = "Fecha", ylab = "Nuevos casos", 
     main = "Nuevos registros en España")
lines(Nuevos_Casos_Muertos ~ Fecha, data = datos_spain[datos_spain$Fecha <= "2020-04-12", ],
      type = "l", col = "red")
lines(Nuevos_Casos_Recuperados ~ Fecha, data = datos_spain[datos_spain$Fecha <= "2020-04-12", ],
      type = "l", col = "green")

legend("topleft", c("Confirmados", "Muertos", "Recuperados"), 
       col = c("blue", "red", "green"), 
       lwd = 2, pch = 1)

Se tomaron mal los datos porque aparecen Nuevos Confirmados y Nuevos muertos negativos por lo que muestro la evolución de nuevos confirmados, nuevos muertos y nuevos recuperados hasta el 12 de abril de 2020 (igual que en el curso).

Análisis por Cohortes

Días desde el Origen de la pandemia

primer_contagio = datos %>%
  group_by(Pais_Region) %>%
  filter(Casos_Confirmados > 0) %>%
  summarise(Primer_Contagio = min(Fecha - 1)) # Fecha de primer contacto de cada país

data_first =  datos %>%
  inner_join(primer_contagio, by = "Pais_Region") %>%
  mutate(Dias_Desde_PC = as.numeric(Fecha - Primer_Contagio)) %>%
  filter(Dias_Desde_PC >= 0) %>%
  group_by(Dias_Desde_PC, Pais_Region) %>%
  summarise(Casos_Confirmados = sum(Casos_Confirmados),
            Casos_Muertos = sum(Casos_Muertos),
            Casos_Recuperados = sum(Casos_Recuperados),
            Casos_Enfermos = sum(Casos_Enfermos))

## `summarise()` has grouped output by 'Dias_Desde_PC'. You can override using the
## `.groups` argument.

Representación Gráfica del Cohorte

data_first %>%
  #filter(Pais_Region %in% c("Spain", "Italy", "China", "US", "Germany")) %>%
  ggplot(aes(x = Dias_Desde_PC, y = Casos_Confirmados)) +
  geom_line(aes(col = Pais_Region)) +
  xlab("Días desde el primer contagio") +
  ylab("Número de personas contagiadas") +
  ggtitle("Análisis por Cohortes") +
  theme(legend.position = "none") -> g

ggplotly(g)

Análisis COVID-19

Jesus Mudarra Luján

2022-08-22